HAND POSE RECOGNITION USING BOOSTED LOOK UP TABLES
First Claim
1. A gesture recognition system, comprising:
- a capture device receiving image data including at least depth data; and
a processor operably coupled to the capture device including code operable to instruct the processor to classify a gesture of a human body in sample image data received by the capture device using a classifier based on a discriminative ferns ensemble.
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Accused Products
Abstract
Pose and gesture detection and classification of a human poses and gestures using a discriminative ferns ensemble classifier is provided. Sample image data in one or more channels includes a human image. A processing device operates on the sample image data using the discriminative ferns ensemble classifier. The classifier has set of classification tables and matching bit features (ferns) which are developed using a first set of training data and optimized by a weighting of the tables using an SVM linear classifier configured based on the first or a second set of pose training data. The tables allow computation of a score per pose class for the image in the sample data and the processor outputs a determination of the pose in the sample depth image data. The determination enables the manipulation of a natural user interface.
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Citations
20 Claims
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1. A gesture recognition system, comprising:
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a capture device receiving image data including at least depth data; and a processor operably coupled to the capture device including code operable to instruct the processor to classify a gesture of a human body in sample image data received by the capture device using a classifier based on a discriminative ferns ensemble. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14)
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11. A computer implemented method of classifying sample image data to determine a gesture present in the sample image data, the method comprising:
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creating a discriminative ferns ensemble classifier having direct indexing to a set of classification tables, the tables developed using a learned model based on a first set of training data and optimized by a weighting of the tables using an SVM linear classifier based on at least one of one of the first set and a second set of training data; receiving sample image data to be classified from a capture device, the capture device including one or more input channels; analyzing the sample image data using the discriminative ferns ensemble classifier; and outputting a determination of the gesture in the sample image data, the determination enabling a manipulation of a natural user interface. - View Dependent Claims (12, 15)
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16. A pose detection and classification system adapted to classify human poses in sample image data, comprising:
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a capture device including a first input channel and a second input channel, each channel providing sample image data; a processing device operable on the sample image data using a discriminative ferns ensemble classifier having direct indexing to a set of classification tables, the tables developed using a first set of training data and optimized by a weighting of the tables using an SVM linear classifier configured based on a second set of training data, the processing device a outputting a determination of the pose in the sample image data, the determination enabling a manipulation of a natural user interface. - View Dependent Claims (17, 18, 19, 20)
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Specification